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Calibrating floor field cellular automaton models for pedestrian dynamics by using likelihood function optimization

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  • Lovreglio, Ruggiero
  • Ronchi, Enrico
  • Nilsson, Daniel

Abstract

The formulation of pedestrian floor field cellular automaton models is generally based on hypothetical assumptions to represent reality. This paper proposes a novel methodology to calibrate these models using experimental trajectories. The methodology is based on likelihood function optimization and allows verifying whether the parameters defining a model statistically affect pedestrian navigation. Moreover, it allows comparing different model specifications or the parameters of the same model estimated using different data collection techniques, e.g. virtual reality experiment, real data, etc. The methodology is here implemented using navigation data collected in a Virtual Reality tunnel evacuation experiment including 96 participants. A trajectory dataset in the proximity of an emergency exit is used to test and compare different metrics, i.e. Euclidean and modified Euclidean distance, for the static floor field. In the present case study, modified Euclidean metrics provide better fitting with the data. A new formulation using random parameters for pedestrian cellular automaton models is also defined and tested.

Suggested Citation

  • Lovreglio, Ruggiero & Ronchi, Enrico & Nilsson, Daniel, 2015. "Calibrating floor field cellular automaton models for pedestrian dynamics by using likelihood function optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 308-320.
    • Handle: RePEc:eee:phsmap:v:438:y:2015:i:c:p:308-320
    DOI: 10.1016/j.physa.2015.06.040
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    Cited by:

    1. Xia Zhong Zheng & Dan Tian & Ming Zhang & Chaoran Hu & Liyang Tong, 2019. "A Stairs Evacuation Model Considering the Pedestrian Merging Flows," Discrete Dynamics in Nature and Society, Hindawi, vol. 2019, pages 1-11, December.
    2. Liu, Yixue & Mao, Zhanli, 2022. "An experimental study on the critical state of herd behavior in decision-making of the crowd evacuation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    3. Lovreglio, Ruggiero & Fonzone, Achille & dell’Olio, Luigi, 2016. "A mixed logit model for predicting exit choice during building evacuations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 59-75.
    4. Zeng, Fanqi & Bode, Nikolai & Gross, Thilo & Homer, Martin, 2024. "Unsupervised pattern and outlier detection for pedestrian trajectories using diffusion maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    5. Korbmacher, Raphael & Dang, Huu-Tu & Tordeux, Antoine, 2024. "Predicting pedestrian trajectories at different densities: A multi-criteria empirical analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    6. Lovreglio, Ruggiero & Spearpoint, Michael & Girault, Mathilde, 2019. "The impact of sampling methods on evacuation model convergence and egress time," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 24-34.

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